Imitation Learning for Variable Speed Contact Motion for Operation up to Control Bandwidth

Sakaino, Sho; Fujimoto, Kazuki; Saigusa, Yuki; Tsuji, Toshiaki

doi:10.1109/ojies.2022.3149333

Cited by 5 publications

(2 citation statements)

References 43 publications

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“…Bilateral control-based imitation learning has been proposed as a method that can compensate for this control delay [16], [17]. We indicated that this method can generate variable speed motions that consider the dynamics between the robot and environment [18], [19]. Although this method is expected to enable nonprehensile manipulations at high speed and multiple speeds, it is difficult to learn complex dynamics between objects and the environment from sensor data and requires a significant amount of training data.…”

Section: Introductionmentioning

confidence: 99%

Imitation Learning for Nonprehensile Manipulation Through Self-Supervised Learning Considering Motion Speed

2022

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Robots are expected to replace menial tasks such as housework. Some of these tasks include nonprehensile manipulation performed without grasping objects. Nonprehensile manipulation is very difficult because it requires considering the dynamics of environments and objects. Imitating complex behaviors requires a large number of human demonstrations. In this study, a self-supervised learning that considers motion speed to achieve variable speed for nonprehensile manipulation is proposed. The proposed method collects and fine-tunes only successful actions among the data obtained during autonomous operations. By fine-tuning the successful data using speed labels, the robot learns the dynamics among itself, its environment, and objects. We experimented with the task of scooping and transporting pancakes using the neural network model trained on 24 human-collected training data. The proposed method significantly improved the success rate from 40.2% to 85.7%, and succeeded more than 75% for other objects.

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Section: Introductionmentioning

confidence: 99%

Imitation Learning for Nonprehensile Manipulation Through Self-Supervised Learning Considering Motion Speed

2022

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“…Automation of tasks with changing contact states, such as assembly and grinding, which are called contact-rich tasks, are widely studied in robotics [1]- [4]. In contact-rich tasks, the contact force with the environment changes significantly depending on the contact states; hence, force-sensing methods for detecting contact force are important.…”

Section: Introductionmentioning

confidence: 99%

Contact Force Detection of Grinding Process Using Frequency Information and Differential Feature on Force Signal

2022

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In grinding tasks, the contact force has a significant impact on product surface quality. Therefore, force-sensing technology to detect contact force is important. Although force sensors are widely used for contact force detection, the response of the force sensor includes sensor-specific errors such as offset. In this paper, we propose a contact force detection method based on the combination of frequency information and the differential feature (∆F ) of the force signal. The use of high-frequency information reduces the influence of force sensor-specific errors. However, contact force detection using only highfrequency information causes a time delay in the detected value relative to the measured value depending to the frame size of time window used for frequency analysis. To reduce the time delay, high-frequency information and ∆F are integrated by inputting them into an long short-term memory (LSTM)-based force detection model. To verify the effectiveness of the proposed method, we compared it with a force detection model based on an FNN and CNN on a dataset of plane grinding tasks. Consequently, the detection accuracy of the LSTM-based model was superior to that of the FNN and CNN models. Compared to the LSTM model using only high-frequency information as input, the detection accuracy was 26% higher when the error was small and 57% higher when the error was large. In addition, the time delay was reduced from 166 ms to 30 ms using ∆F as the input. The frequency information and ∆F are features calculated from the same force information dataset; therefore, no additional dataset is required.INDEX TERMS Force sensing, force control, machine learning for robot control, grinding.

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Air Combat Game Based on Behavior Repetition

Zhang

Benzhang

et al. 2023

Lecture Notes in Electrical Engineering

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Imitation Learning for Variable Speed Contact Motion for Operation up to Control Bandwidth

Cited by 5 publications

References 43 publications

Imitation Learning for Nonprehensile Manipulation Through Self-Supervised Learning Considering Motion Speed

Imitation Learning for Nonprehensile Manipulation Through Self-Supervised Learning Considering Motion Speed

Contact Force Detection of Grinding Process Using Frequency Information and Differential Feature on Force Signal

Air Combat Game Based on Behavior Repetition

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